The wave of digital transformation is on the horizon. Businesses from all realms are all fired up and ready to begin their plans for the implementation of AI technology. The term “AI” has been one of the top searches on the Internet in the past few years and continues to grow. Let’s look at a survey conducted by AI Academy Taiwan from last October, about the current state of AI application in businesses. The faculty analyzed answers from 516 businesses and found that 86% of the businesses believe that their company needs the implementation of AI technology. However, is the world ready for this? In this article, we are sharing CloudMile’s past experiences in helping more than 25 businesses implementing AI technology in their operation and boosting their performance.
The Foundation of AI Implementation: Data, Data, and Data
In the “The Secret of CloudMile! 3 Essential Traits Companies Look For in AI Specialists Recruitment “article, we have used coffee beans as an analogy for data. It represents the most important material you need before proceeding to machine learning. Without data, you will not be able to make an aromatic cup of coffee. Take the application of E-commerce for example, all actions taken by customers on websites like browsing and dealing from various platforms, from visit frequency to visit duration, and to clicks as well as downloads, these are all precious data assets.
After the accumulation of all types of data, it is very important to keep one thing in mind. The accuracy of your data can strongly affect the final results of machine learning. The situation of “Garbage in, garbage out” must always be avoided. With data loss, incomplete data and outdated data, you will develop a faulty data model. That’s why data cleansing is key to machine learning.
To understand what the so called data cleansing means, the best way is to look at the following example. For instance the membership information on E-commerce websites, some sites collect members’ birthday info in the common era calendar format and others use the Taiwanese civil calendar. For this reason, when going through data cleansing and organization, we would usually rewrite the values that used Taiwanese civil calendar or recollect the missing data column. This is to help avoid developing a faulty prediction model due to inaccuracies in data and further increase the effectiveness of an AI project.
Define Problems Definitively and Work With the Right Teams and Talents
Do companies need a professional AI team for AI implementation in their businesses? Or should they consider outsourcing? This answer may vary depending on the core value of your products, the target issues you wish to solve, and the goals you wish to achieve. A 2019 survey regarding AI leading indicators in business stated that more than 86% of the financial industry in Taiwan applies the method of cross-department team working. They would also reach out for external resources or outsource their AI analysis projects. One of the reasons behind this is that after evaluation, the cost of developing an AI resolution project is often too high, which leads to the outsourcing trend from the companies. The second reason for outsourcing is that the process involves context specific development of the financial sector and internal private data training model. Thus, it would be more efficient to opt for a customized AI project and further develop an exclusive analysis tool.
Let us take a look at the past examples of CouldMile’s services for the Textile Manufacturers industry; We learned that our customer have adopted the old Warehouse Management System in the past. Also, they used to rely on visual inspection on their fabric, which led to the drop of their efficiency. After many consulting sessions, we guided our customers to define clear specifications: Speed detection and a quicker search for materials. We then trained a fabric detection model with machine learning technology. This sufficiently reduced the manufacturing process and boosted the production efficiency.
The Management Level Should Guide Their Team to Better Understand AI and Start Small
A common myth for businesses is their impractical expectations towards AI technology, they all expect to see immediate results. However, there are many points to consider when kicking off a project. From defining the specifications and data mining in the initial stage to data cleansing and model developing in the mid stage, and eventually to the releasing stage. This process often requires at least six months of effort. Other reasons why the process may take longer, are the lack of interior data from business or when their IT infrastructure failed to support. Thus, AI Academy Taiwan CEO Sheng Wei Chen suggests, the management level from businesses should gain a correct perception towards AI solutions. Do not fall into the old concept of the traditional ROI system, think outside the box. Keep your eyes on the prize, seize the chance to successfully transform your business.
Google Deep Learning Research Team co-founder Wu En Da indicates that, providing all level of employees with proper AI training can greatly help a business transform, and further train AI talents from within. He then pitch out the idea of “Start Small” with the development AI technology. First, apply atomization and digitization in repetitive and tedious operation. Then advance to the application of AI technology. On the other hand, starting small also means to build smaller teams. Nail down clear and definite goals, milestones and a reasonable budget to wisely examine the investment outcome.